Scalable and reliable wireless sensor network systems
description
Transcript of Scalable and reliable wireless sensor network systems
Scalable and reliable wireless sensor network systems
Vinod Kulathumani
Dept. of Computer Science and Electrical EngineeringWest Virginia University
CS/EE 796 Graduate seminar series
Embedded systems
Found in variety of devices Aircraft, radar systems, nuclear and chemical plants Vehicles, TVs, camcorders, elevators > 90% of CPUs used for embedded devices
Part of a larger system Application known apriori
Little flexibility in programming
Networked embedded systems
What if embedded processors were connected ? Not wired but wireless
Enter Wireless Sensor Networks
- Really a network of embedded systems
Enabling technology
Micro-sensors (MEMS, Materials, Circuits) acceleration, vibration, gyroscope, tilt, motion magnetic, heat, pressure, temp, light, moisture, humidity, barometric chemical (CO, CO2, radon), biological, micro-radar actuators (mirrors, motors, smart surfaces, micro-robots)
Communication short range, low bit-rate, CMOS radios
The Vision for WSNs
Combine wireless networks with sensing / actuation
Ubiquitous computing Fine-grained monitoring and control of environment Network and interact with billions of embedded computers
Reasons Wireless communication - no need for infrastructure setup Drop and play Nodes are built using off-the-shelf cheap components Feasible to deploy nodes densely
A new class of computing
year
log
(p
eo
ple
pe
r c
om
pu
ter)
streaming informationto/from physical world
Number CrunchingData Storage
productivityinteractive
Mainframe
Minicomputer
Workstation
PC
Laptop
PDA
Slide courtesy: Murat Demirbas
Application areas
Science: oceanography, seismology
Engineering: industrial automation, structural monitoring
Daily life: health care, disaster recovery
Emerging applications
Combination of sensors with mobile devices Social networking Participatory urban sensing
Assisted living – health monitoring Vehicular networks with variety of sensors Control systems using sensor networks
Trends
Increasing in scale
Increasing in complexity Middle America Subduction Experiment
ExScalIntel Developer Forum
Intel Hillsboro Fab
Outline of talk
Research challenges / goals
Summary of contributions Centralized classification / tracking [SRDS’05, Computer Comm’03] Distributed vibration control [MSNDC’05] Sensor network service for object tracking [EWSN’07, IPSN’06] Distance sensitive snapshot service [OPODIS’07]
Details of a specific contribution Sensor network service for object tracking
Future research interests
My research focus
Interests
Distributed systems / networking Fault-tolerance Self-healing systems Scalability
Sensor networks pose plenty of problems in these areas !
Research challenge
Application
Network
Resource constrained nodes
Low bandwidth, fading, interference
Harsh, malicious environments
Network abstraction layerMiddleware services
Network design
How to design scalable, reliable WSN applications despite unreliable networks ?
Rising in scale, complexity
Performance crucial
Industrial, medical, military
Observation based / control based
Static / mobile
Scales: < 100 to 10000
Unreliable
Classification and tracking (monitoring)
Scenario – asset protection Dense deployment; Resource and bandwidth constrained Goal: classify and observe tracks of objects
Application design Reliable estimation of influence fields [SRDS ‘05] Influence field (IF) – region over which an object can be detected IF estimated using binary detections Classification – Estimating size of IF Tracking – Estimating shape of IF
Soldier and vehicle influence fields wrt magnetometer
Scenario – asset protection Dense deployment; Resource and bandwidth constrained Goal: classify and observe tracks of objects
Application design Reliable estimation of influence fields [SRDS ‘05]
Network design Network abstractions for IF separation
Distance insensitivity, contention insensitivity
Network abstractions for IF shape Routing uniformity
Network parameters (density)
Aggregator
Scenario – asset protection Dense deployment; Resource and bandwidth constrained Goal: classify and observe tracks of objects
Application design Reliable estimation of influence fields [SRDS ‘05]
Network design Network services for separation Network services for uniformity Network parameters (density)
Deployment and testing Line in the sand [Computer Communications’ 03] ExScal (RTSS’05)
Scenario – asset protection Dense deployment; Resource and bandwidth constrained Goal: classify and observe tracks of objects Requirement : low latency (<2 s), high accuracy (> 99%)
Distributed vibration control
Scenario Control vibrations during payload launch Sensors / actuators distributed across surface Low computational resource, fault-prone Experimental study on Boeing fairing simulator [MSNDC’05]
Faults impact – potentially severe Hard to detect in real time
Requirement – mission critical stability
Scenario Control vibrations during payload launch Sensors / actuators distributed across surface
Application design Use on-off control scheme Model plant as linear system; vibration modes assumed Model unreliability as Byzantine behavior of actuators
Worst input to plant at all times
Scenario Control vibrations during payload launch Sensors / actuators distributed across surface
Application design Use on-off control scheme Model plant as linear system; vibration modes assumed Model unreliability as Byzantine behavior of actuators
Worst input to plant at all times
Network design Determine actuator placement for plant to be stable despite
Byzantine actuators [MSNDC’ 05]
Distributed tracking – optimal interception
Scenario WSN laid to protect asset Evader’s goal: minimize distance to asset Pursuer’s goal: intercept evaders at maximum distance Pursuers query sensor network for mobile evader locations
Scenario WSN laid to protect asset Pursuers query sensor network for mobile evader locations
Application design Model as zero-sum game Formulation of optimal pursuit control strategies [IPSN’06]
Presence of delay Under discrete sampling rate
Nash equilibrium conditions for successful pursuit
information of nearer objects required at faster rate
information of nearer objects required with lower delay
Scenario WSN laid to protect asset Pursuers query sensor network for mobile evader locations
Application design Model as zero-sum game Formulation of optimal pursuit control strategies [IPSN’06]
Network design Trail – a distance sensitive network service O(d) find time, cost for object distance d away O(d*log(d)) update time, cost for distance d moved Fault-tolerant, energy-efficient, family of tunable protocols
Scenario WSN laid to protect asset Pursuers query sensor network for mobile evader locations
Application design Model as zero-sum game Formulation of optimal pursuit control strategies [IPSN’06]
Network design Trail – a distance sensitive network service
Deployed and tested in Catch Me If You Can Demonstrated at Richmond Field Station, Berkeley, August 05
Distance sensitive snapshots in WSN
Scenario Distributed object tracking using WSN Goal: Pursuers should eventually catch all evaders
Application design Perfect information not necessary State of evaders distance sensitive in error, latency and rate
eventual catch
Network design Network service for distance sensitive snapshots [OPODIS 07] Exploit alternate forms of compression to gain efficiency
State of nearby nodes is fresher State of nearby nodes more precise State of nearby nodes refreshed more often
Systems built
ExScal (Extreme Scaling Experiment) Goal: classify between person, soldier, SUV and ATV and track Deployment area: 1,260m x 288m 1000+ sensor nodes, 200+ Stargates Technology transferred to Northrup Grumman
10,000 node experiment using ExScal software
Roles Classification / tracking subsystem Integrating communication chain Yield studies [ICNP’05]
Identify and study impact of faults
ExScal field
Other systems built
Kansei WSN testbed at Ohio State 432 TelosB, 150 Stargates, 150 XSM, 100
i-mote2 Software services for data injection, data
collection
Mobile network PeopleNET Cellphones integrated with psi-mote Buddy messaging, elevator status
Vehicle classification Los Alamos National Labs [2007] Seismic + Acoustic sensors
Trail: network service for tracking
Motivating scenario
Mobile Objects tracked by network of static sensors over a large area Network runs a tracking service Application (residing on mobile objects) issues query of the
form “Find object X” to the tracking service
Motivation for Trail
Queries answered by one (or more) central nodes not scalable Depletes energy Increases latency
One way to make queries local Publish object state everywhere But upon every move, global update needed
Global update for every object move not scalable
We need to publish object information systematically
Informal problem statement
Network tracking service returns query results in time and work proportional to distance from object
Requirement 1: Find distance sensitivity
When an object moves, tracking protocol updates the track in time and work proportional to distance moved
Requirement 2: Update distance sensitivity
Trail tracking structure
Trail protocol based on geometric ideas Properties proved on continuous 2-d plane Then implemented on discrete plane
Model 2-d real bounded plane, C denotes center of this plane Cost measured in Euclidean distance
One track maintained for each object Let P be object being tracked located at point p Tracking data structure for P denoted as trailP
Pointers that lead to current location of P
All tracks rooted at C
Trail intuition
If trailP restricted to be a straight line, each move will involve update from C
C
p’
p
Instead, trailP marked with vertices on-the-fly Vertices serve as anchor points for update Distance between vertices increases exponentially moving
towards C Anchor updated depending on distance moved After sufficiently large distance, update from C
Examples of trailPC
N3
N2
p
N1
c3c2c1
N3
N2
p
N1
c3c2 c1
C
N3
N2
p
N1
c3c2 c1
CC
N3
N2
p
N1
c3
c2
c1
N3
N2
N1
c3
c2
c1
p
CC
N3
N2
p
N1
c3
c2
c1
Cost for update and find
Cost of updating trailP over a move of distance d is O(d*log(d))
Theorem N3
N2
p’
N1
c3
c2
c1
p
worst case structure: log spiral
Algorithm for find
Cost of finding P from object Q at point q is O(d) where d is dist(p,q)
Theorem
C
p c2
N3
N2
N1
c3
q
m
Draw successive circles of radii 20, 21, 22 .. 2(log dist(C,q)) Until trailP is intersected
Or reach C
Follow trailP to reach current location of P
Cost includes reaching trailP, following trailP, returning to q
Fault-tolerance and adaptivity of Trail
Fault-tolerance Nodes may fail after creating trail or old trails may not be deleted
Self-stabilizing actions using heartbeats along trail structure
Tolerating failures during update and find Route around failures using a method such as left hand rule in GPSR
As size of holes increases, update and find cost proportionally increase Trail supports graceful degradation
Adaptivity (Trail yields family of protocols) Can be tuned based on update and query frequency When query frequency higher, publish structure increases and find
increasingly straight Extreme case – find is a straight line to C and publish in circles
Performance evaluation
Experimental evaluation (Kansei testbed at OSU) Used to demonstrate PE tracking application for NEST DARPA project
Intruder tracks collected from Richmond Field Station [140m X 60m]
Tracks injected into Kansei testbed nodes to emulate motion of
evaders 15 X 7 node network, 3 ft spacing
3 pursuer 3 evader scenario
Study effect of interference on scaling in Objects [2 - 10] Query frequency [0.25 – 1 Hz]
Simulations [JProwler] 8100 nodes (90 by 90)
Up to 50 objects (uniformly separated and collocated)
Garcia Robots as Pursuers
Summary of Trail features
Trail – a distance sensitive network service Assumes no hierarchical partitioning of network O(d) find time, cost for object distance d away O(d*log(d)) update time, cost for distance d moved Fault-tolerant
Self-stabilizing, graceful degradation
When many objects come close together, network interference can cause delay Synchronized push version? Distance sensitive snapshot service
Distance sensitive snapshot service
A brief overview
Informal problem statement
Given N nodes, with bounded memory, in f dimensions each can sense m-bit information at any time each can communicate at W bits per second
Deliver a global snapshot at each node (can be relaxed to a subset) that uniformly has distance sensitive latency (and distance sensitive
resolution, and distance sensitive rate) State of nearby nodes is fresher State of nearby nodes more precise State of nearby nodes refreshed more often
periodically, as fast as possible (can be relaxed to lower rate)
Illustration
Illustration
Results
Maximum staleness in state of a node i received by a
snapshot at node j is O(log(n) * m * d) where d = dist(i, j)
Resolution of state of a node i in a snapshot received at node
j is Ω(1 / d2) where d = dist(i, j)
Communication cost to deliver a snapshot of one sample
from each node to all nodes is on average O(N * log(n) * m)
Conclusions
Research focus Reliable network services for WSN applications
Applications for classification, tracking, distributed control
Network services tested in actual field deployments
Key role in integrating complete WSN systems ExScal, Line in the Sand, Kansei, Catch Me If You Can
Facility monitoring at Los Alamos National Labs
Provided deep insight into real problems in wireless and sensor
networks
Future research interests
WSNs combined with mobility, actuation
Mobile heterogeneous wireless networks
Convergence of mobile devices with sensors Urban surveillance, online health monitoring, disaster relief, mobile
gaming, vehicular networks
Realization of ubiquitous systems
Research questions Low power self – localization of mobile units
Scenarios: low cost indoor tracking, security, identity management
Reliable, secure information management Protect against eavesdropping, jamming
Provide reliable content delivery
Architecture Composing applications across heterogeneous networks [MODUS 2008]
Convergence / inter-operability with Internet, cellular networks
Wireless sensor networks for control
WSNs suited for control applications Wireless feature: industrial control and process control applications
Large scale feature: control of distributed parameter systems, power grids
Challenges / research questions Performance
How to guarantee reliability / low latency and meet wire-line standards?
How to secure the network against jamming?
Architecture Underlying network independent of control system / application ?
Theory Joint stabilization of control application and network layer
Cross cutting research
Network protocolsNetwork architecture
•Reliable•Secure
Information processing Control systems
Computer vision (urban surveillance)
Wireless communication technology
MEMS / sensor fabrication
Database systemsData Mining
Thank you
Contact Information
Vinod Kulathumani
http://www.csee.wvu.edu/~vkkulathumani